A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
/corral4/main/jobs/066/693/66693077/working/multiqc_WDir
General Statistics
| Sample Name | Duplication | Error rate | Non-primary | Reads mapped | % Mapped | % Proper pairs | % MapQ 0 reads | Total seqs | Mean insert | % Duplication | % > Q30 | Mb Q30 bases | Reads After Filtering | GC content | % PF | % Adapter |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SRR11954102 | 0.7% | 0.32% | 0.0M | 0.0M | 100.0% | 100.0% | 0.0% | 0.0M | 156.7bp | 0.0% | 90.2% | 212.4Mb | 2.7M | 51.8% | 90.7% | 26.5% |
| SRR12733957 | 14.3% | 1.02% | 0.0M | 0.0M | 100.0% | 100.0% | 0.0% | 0.0M | 184.9bp | 0.0% | 82.7% | 49.4Mb | 0.6M | 48.4% | 68.0% | 1.0% |
Picard
Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard
Mark Duplicates
Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.
The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.
To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:
READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATESREADS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATESREADS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATESREADS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICALREADS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATESREADS_UNMAPPED = UNMAPPED_READS
Samtools
1.20
Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352
Percent mapped
Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.
For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.
Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).
Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.
Alignment stats
This module parses the output from samtools stats. All numbers in millions.
fastp
0.24.0
All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560
Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.Filtered Reads
Filtering statistics of sampled reads.
Insert Sizes
Insert size estimation of sampled reads.
Sequence Quality
Average sequencing quality over each base of all reads.
GC Content
Average GC content over each base of all reads.
N content
Average N content over each base of all reads.
Software Versions
Software Versions lists versions of software tools extracted from file contents.
| Software | Version |
|---|---|
| Samtools | 1.20 |
| fastp | 0.24.0 |
